Abstract

While glycans are crucial for biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include these diverse carbohydrates into workflows. Here, we present glycowork, an open-source Python package designed for glycan-related data science and machine learning by end users. Glycowork includes functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models, and learned glycan representations. We envision that glycowork can extract further insights from glycan datasets and demonstrate this with workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

中文翻译：

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Glycowork：用于聚糖数据科学和机器学习的 Python 包

 抽象的

虽然聚糖对于生物过程至关重要，但现有的分析模式使得计算背景有限的研究人员很难将这些不同的碳水化合物纳入工作流程。在这里，我们介绍 gluwork，这是一个开源 Python 包，专为最终用户设计与聚糖相关的数据科学和机器学习。 Glycowork 包括自动注释聚糖基序并通过热图和统计富集分析其分布等功能。我们还提供可视化方法、与存储的数据库交互的例程、经过训练的机器学习模型和学习的聚糖表示。我们设想 gluwork 可以从聚糖数据集中提取更多见解，并通过分析各种生物背景下的聚糖基序的工作流程来证明这一点。 Glycowork 可以在 https://github.com/BojarLab/gluwork/ 上免费访问。